College English vocabulary learning faces the problems of blindness, unplannedness and lack of vocabulary learning strategies. The traditional learning system is unable to provide personalized service for students’ individual differences. In this paper, to address the problems of blindness and lack of strategies in college English vocabulary learning, we constructed an adaptive learning system with a hybrid recommendation algorithm integrating knowledge graph and collaborative filtering to provide personalized English vocabulary learning paths for college students. The study adopts TransR model and LSTM model combined with Self-Attention attention mechanism for knowledge graph representation learning, and integrates it with collaborative filtering algorithm with improved cosine similarity computation method to achieve personalized learning resources recommendation. To verify the effectiveness of the system, the study recruited 64 college students for a 7-day English vocabulary learning experiment, and divided them into a control group using the traditional learning system and an experimental group using the adaptive learning system. The results show that the adaptive learning system scored 23.07 in the dimension of resource recommendation effectiveness, which is significantly higher than the 15.18 of the traditional learning system; the students in the experimental group scored 20.54 in vocabulary mastery, which is significantly higher than the 15.83 of the control group; and the experimental group’s score in vocabulary writing reached 22.86, which is 6.77 higher than that of the control group. The conclusion shows that the adaptive learning system based on the hybrid recommendation algorithm of knowledge graph and collaborative filtering can effectively identify the individual differences of learners, provide targeted learning resources, improve the learning efficiency, significantly enhance the effect of college students’ English vocabulary learning, and provide a new way for personalized learning of college English vocabulary.
